Abstract

Rainfall prediction is a challenging problem in the meteorological department around the world due to the accurateness of prediction. This paper studies on data mining techniques to predict rainfall using meteorological data of Subang Weather Station collected from January 2009 to December 2016. The data preparation process involves five weather factors which are maximum temperature, minimum temperature, evaporation, wind speed and cloud with 2922 observations. Predictive Decision Tree model, Artificial Neural Network model and Naïve Bayes model are developed for rainfall prediction and comparison. Surprisingly, results show that the performance of Decision Tree model is better as compared to the other predictive models with the misclassification rate of 0.15 and RMSE=0.35. Given enough set of data, rainfall can be predict using the data mining techniques.

Language

English

Title of host publication

Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018

abstract = "Rainfall prediction is a challenging problem in the meteorological department around the world due to the accurateness of prediction. This paper studies on data mining techniques to predict rainfall using meteorological data of Subang Weather Station collected from January 2009 to December 2016. The data preparation process involves five weather factors which are maximum temperature, minimum temperature, evaporation, wind speed and cloud with 2922 observations. Predictive Decision Tree model, Artificial Neural Network model and Na{\"i}ve Bayes model are developed for rainfall prediction and comparison. Surprisingly, results show that the performance of Decision Tree model is better as compared to the other predictive models with the misclassification rate of 0.15 and RMSE=0.35. Given enough set of data, rainfall can be predict using the data mining techniques.",

N2 - Rainfall prediction is a challenging problem in the meteorological department around the world due to the accurateness of prediction. This paper studies on data mining techniques to predict rainfall using meteorological data of Subang Weather Station collected from January 2009 to December 2016. The data preparation process involves five weather factors which are maximum temperature, minimum temperature, evaporation, wind speed and cloud with 2922 observations. Predictive Decision Tree model, Artificial Neural Network model and Naïve Bayes model are developed for rainfall prediction and comparison. Surprisingly, results show that the performance of Decision Tree model is better as compared to the other predictive models with the misclassification rate of 0.15 and RMSE=0.35. Given enough set of data, rainfall can be predict using the data mining techniques.

AB - Rainfall prediction is a challenging problem in the meteorological department around the world due to the accurateness of prediction. This paper studies on data mining techniques to predict rainfall using meteorological data of Subang Weather Station collected from January 2009 to December 2016. The data preparation process involves five weather factors which are maximum temperature, minimum temperature, evaporation, wind speed and cloud with 2922 observations. Predictive Decision Tree model, Artificial Neural Network model and Naïve Bayes model are developed for rainfall prediction and comparison. Surprisingly, results show that the performance of Decision Tree model is better as compared to the other predictive models with the misclassification rate of 0.15 and RMSE=0.35. Given enough set of data, rainfall can be predict using the data mining techniques.